December 7, 2015
\(P(y) = ...\)
\(P(y = 0)\) if \(y = 0\)
\(( 1 - P(y = 0)) P(y)\) if \(y > 0\)
\[p_{f,s,y} = \gamma_{1}fished_{f} + \gamma_{2}mpa_{s,y} + \gamma_{3}FxM_{f,s,y} +\]
\[\sum\gamma_{4}year_{y} + \sum\gamma_{5}trophic_{f} + \gamma_{6}linf_{f} + \]
\[ \gamma_{7}temperature_{s,y} + \gamma_{8}visibility_{s,y}\]
\[ d_{f,s,y} = \beta_{1}fished_{f} + \beta_{2}mpa_{s,y} + \beta_{3}FxM_{f,s,y} + \sum\beta_{4}region_{s} + \]
\[ \sum\beta_{5}trophic_{f} + \beta_{6}yearsmpa_{s,y} + \beta_{7}FxYM_{f,s,y} + \]
\[+ \beta_{7}linf_{f} + \beta_{8}vbk_{f} + \beta_{9}temp_{s,y} + \beta_{10}vis_{s,y} + \]
\[ \beta_{11}templag1_{s,y} + \beta_{12}templag2_{s,y} + \beta_{13}templag3_{s,y} + \beta_{14}templag4_{s,y} + \]
\[\beta_{15} + \sum\beta_{16}year_{y} \]
\[ \sum [dbinom(o_{f,s,y},1,\hat{p_{f,s,y}}, log = T) + \] \[ dnorm(d_{f,s,y},\hat{d_{f,s,y}},\sigma, log = T])^{o_{f,s,y}} ] \]
For now assuming uniform priors on most things except…
\(\sigma\) ~\(TN(0.1,0.2)\)
No significant effect of presence/absence of MPAs on fished species
Over time the MLPA increased fished species density
MPAs are noisy
The demon is slow!
What does hurdle model do to identification?
Hurdle component seems reasonable